Systems and methods for estimating tissue parameters using surgical devices

ABSTRACT

Systems and methods for estimating tissue parameters, including mass of tissue to be treated and a thermal resistance scale factor between the tissue and an electrode of an energy delivery device, are disclosed. The method includes sensing tissue temperatures, estimating a mass of the tissue and a thermal resistance scale factor between the tissue and an electrode, and controlling an electrosurgical generator based on the estimated mass and the estimated thermal resistance scale factor. The method may be performed iteratively and non-iteratively. The iterative method may employ a gradient descent algorithm that iteratively adds a derivative step to the estimates of the mass and thermal resistance scale factor until a condition is met. The non-iterative method includes selecting maximum and minimum temperature differences and estimating the mass and the thermal resistance scale factor based on a predetermined reduction point from the maximum temperature difference to the minimum temperature difference.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalApplication No. 61/882,675, filed on Sep. 26, 2013, U.S. ProvisionalApplication No. 61/882,678, filed on Sep. 26, 2013, and U.S. ProvisionalApplication No. 61/882,680, filed on Sep. 26, 2013. This application isrelated to U.S. patent application Ser. No. ______, filed on ______, andU.S. patent application Ser. No. ______, filed on ______. The entirecontents of the above applications are incorporated herein by reference.

BACKGROUND

1. Technical Field

The present disclosure relates to estimating tissue parameters. Moreparticularly, the present disclosure relates to systems and methods forestimating tissue parameters, such as tissue mass, via surgical devicesand controlling these surgical devices based on the estimated tissueparameters.

2. Background of Related Art

There are many types of surgical devices that may be used to treattissue in a variety of surgical procedures. One type of surgical deviceis a linear clamping, cutting, and stapling device. This device may beemployed in a surgical procedure to resect a cancerous or anomaloustissue from a gastro-intestinal tract. Conventional linear clamping,cutting and stapling instruments include a pistol grip-styled structurehaving an elongated shaft. The distal portion of the elongated shaftincludes a pair of scissors-styled gripping elements, which clamp theopen ends of the colon closed. In this device, one of the twoscissors-styled gripping elements, such as the anvil portion, moves orpivots relative to the overall structure, whereas the other grippingelement remains fixed relative to the overall structure. The actuationof this scissoring device (the pivoting of the anvil portion) iscontrolled by a grip trigger maintained in the handle.

In addition to the scissoring device, the distal portion of theelongated shaft also includes a stapling mechanism. The fixed grippingelement of the scissoring mechanism includes a staple cartridgereceiving region and a mechanism for driving the staples up through theclamped end of the tissue against the anvil portion, thereby sealing thepreviously opened end. The scissoring elements may be integrally formedwith the shaft or may be detachable such that various scissoring andstapling elements may be interchangeable.

Another type of surgical device is an electrosurgical device which isemployed in an electrosurgical system for performing electrosurgery.Electrosurgery involves the application of high-frequency electriccurrent to cut or modify biological tissue. Electrosurgery is performedusing an electrosurgical generator, an active electrode, and a returnelectrode. The electrosurgical generator (also referred to as a powersupply or waveform generator) generates an alternating current (AC),which is applied to a patient's tissue through the active electrode andis returned to the electrosurgical generator through the returnelectrode. The alternating current typically has a frequency above 100kilohertz (kHz) to avoid muscle and/or nerve stimulation.

During electrosurgery, AC generated by the electrosurgical generator isconducted through tissue disposed between the active and returnelectrodes. The tissue's impedance converts the electrical energy (alsoreferred to as electrosurgical energy) associated with the AC into heat,which causes the tissue temperature to rise. The electrosurgicalgenerator controls the heating of the tissue by controlling the electricpower (i.e., electrical energy per unit time) provided to the tissue.Although many other variables affect the total heating of the tissue,increased current density usually leads to increased heating. Theelectrosurgical energy is typically used for cutting, dissecting,ablating, coagulating, and/or sealing tissue.

The two basic types of electrosurgery employed are monopolar and bipolarelectrosurgery. Both types of electrosurgery use an active electrode anda return electrode. In bipolar electrosurgery, the surgical instrumentincludes an active electrode and a return electrode on the sameinstrument or in very close proximity to one another, usually causingcurrent to flow through a small amount of tissue. In monopolarelectrosurgery, the return electrode is located elsewhere on thepatient's body and is typically not a part of the energy delivery deviceitself. In monopolar electrosurgery, the return electrode is part of adevice usually referred to as a return pad.

An electrosurgical generator includes a controller that controls thepower applied to a load, i.e., the tissue, over some period of time. Thepower applied to the load is controlled based upon the power determinedat the output of the electrosurgical generator and a power level set bythe user or a power level needed to achieve a desired effect on thetissue. The power may also be controlled based on other parameters ofthe tissue being treated such as tissue temperature.

SUMMARY

The systems and methods of the present disclosure estimate the mass oftissue and a thermal resistance scale factor or a thermal coefficientbetween the tissue and a surgical instrument, such as sealing jawmembers of an electrosurgical instrument. In the case of electrosurgery,the level of power supplied to the tissue may be controlled based on theestimated mass of the tissue. Estimation can be performed by commonlyavailable microprocessors, field programmable gate arrays (FPGAs),digital signal processors (DSPs), application specific integratedcircuits (ASICs), or programmable DSPs.

In one aspect, the present disclosure features a system that includes anelectrosurgical generator and an energy delivery device, where theelectrosurgical generator and the energy delivery device areelectrically coupled to each other. The electrosurgical generatorincludes an output stage, a plurality of sensors, and a controller. Theoutput stage is configured to generate electrosurgical energy, and theplurality of sensors is configured to sense voltage and current of thegenerated electrosurgical energy. The energy delivery device includesjaw members, each of which has an electrode, and a plurality oftemperature sensors that sense temperatures of the tissue and at leastone of the electrodes of the jaw members. The controller is coupled tothe output stage, the plurality of temperature sensors of the energydelivery device, and includes a signal processor and an outputcontroller.

The signal processor estimates a mass of the tissue being treated andthe thermal resistance scale factors of the tissue and an electrode ofthe energy delivery device based on temperature changes of the tissueand the electrode. The estimated mass and the thermal resistance scalefactors rather than an impedance of the tissue being treated ortogether, as a control variable, may be used to fine tune or to modifyan electrosurgical operation. The output controller controls the outputstage based on the estimated mass of the tissue and the estimatedthermal resistance scale factor.

In embodiments, staplers may use the estimated mass to estimate the sizeor thickness of tissue to be stapled.

The electrosurgical generator may further include analog-to-digitalconverters (ADCs) electrically coupled to the temperature sensors. TheADCs may sample the sensed temperatures to obtain a predetermined numberof samples of the sensed temperatures.

The output controller may generate a control signal based on theestimated mass of the tissue and the estimated thermal resistance scalefactor. The control signal may be used to control the output stage.

The signal processor may sample temperatures of the tissue sensed by theplurality of temperature sensors a predetermined number of times,calculate a temperature difference for each sampled temperature, andestimate mass of the tissue and a thermal resistance scale factorbetween the tissue and the electrode based on the sampled temperaturesand the calculated temperature difference. The signal process mayfurther perform selecting a maximum and a minimum among the calculatedtemperature difference, calculating a time at which a predeterminedpercentage reduction occurs from the maximum to the minimum, calculatean estimate of a thermal resistance scale factor based on the calculatedtime, and calculating a mass estimate based on the estimate of thethermal resistance scale factor and the calculated time.

The temperature sensors may be selected from a resistance temperaturedetector, a thermocouple, a thermostat, and a thermistor.

The present disclosure, in another aspect, features a method ofcontrolling a system that includes a generator that generates energy totreat tissue. The method includes providing a test signal to the tissue,sensing temperatures of an electrode of an energy delivery device andtissue to be treated a predetermined number of times, calculating atemperature difference for each sensed temperature value, estimating amass of the tissue and a thermal resistance scale factor between thetissue and the electrode, and generating a control signal to control anoutput stage of the generator based on the estimated mass and theestimated thermal resistance scale factor.

The mass of the tissue and the thermal resistance scale factor areestimated based on the sensed temperatures and the calculated changes intemperature. Estimating the mass of the tissue and the thermalresistance scale factor may include calculating an initial mass estimateand an initial thermal resistance scale factor estimate for each sensedtemperature, selecting one of the initial mass estimates as a startingmass estimate and one of the initial thermal resistance scale factorestimates as a starting thermal resistance scale factor estimate,setting a first derivative step for the mass estimate and a secondderivative step for the thermal resistance scale factor estimate, andperforming an iterative method to estimate the mass and thermalresistance scale factor of the tissue using the starting mass estimate,the starting thermal resistance scale factor estimate, and the first andsecond derivative steps.

The iterative method may be a gradient descent method that includescalculating a first temperature estimate and a first temperaturedifference estimate based on the mass estimate and the thermalresistance scale factor estimate, calculating a second temperatureestimate and a second temperature difference estimate based on the massestimate, the thermal resistance scale factor estimate, and a firstderivative step for the mass estimate, calculating a third temperatureestimate and a third temperature difference estimate based on the massestimate, the thermal resistance scale factor estimate, and a secondderivative step for the thermal resistance scale factor estimate,calculating first errors between the sensed temperature and the firsttemperature estimate, between the sensed temperature and the secondtemperature estimate, between the sensed temperature difference and thefirst temperature difference estimate, and between the sensedtemperature difference and the second temperature difference estimate,calculating second errors between the sensed temperature and the firsttemperature estimate, between the sensed temperature and the thirdtemperature estimate, between the sensed temperature difference and thefirst temperature difference estimate, and between the sensedtemperature difference and the third temperature difference estimate,calculating a first error derivative based on the calculated firsterrors, calculating a second error derivative based on the calculatedsecond errors, calculating an updated mass estimate based on the firsterror derivative, and calculating an updated thermal resistance scalefactor estimate based on the second error derivative.

Controlling the electrosurgical energy includes generating a controlsignal to control the output stage of the generator based on theselected mass of the tissue and the selected thermal resistance scalefactor.

Calculating an updated mass estimate includes determining whether thefirst error derivative changes sign, reducing the first derivative stepwhen it is determined that the first error derivative changes in sign,and setting the mass estimate as the sum of the mass estimate and thefirst derivative step.

Calculating an updated thermal resistance scale factor estimate includesdetermining whether the second error derivative changes in sign,reducing the second derivative step when it is determined that thesecond error derivative changes sign, and setting the thermal resistancescale factor estimate as the sum of the thermal resistance scale factorestimate and the second derivative step.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure are described withreference to the accompanying drawings wherein:

FIG. 1 is an illustration of an electrosurgical system in accordancewith embodiments of the present disclosure;

FIG. 2 is a block diagram of a generator circuitry of theelectrosurgical generator of FIG. 1 and an energy delivery deviceconnected to the generator circuitry;

FIG. 3 is a schematic diagram of the controller of FIG. 2;

FIG. 4A is a front cross-sectional view of a jaw member assembly of anelectrosurgical forceps of FIG. 1, which incorporates temperaturesensors;

FIG. 4B is a perspective view of a stapling instrument according toembodiments of the present disclosure;

FIG. 5 is a flow diagram illustrating a method of estimating tissue massand the thermal resistance scale factor that may be performed by thedigital signal processor of FIG. 2 in accordance with some embodimentsof the present disclosure;

FIGS. 6A-6D are flow diagrams illustrating a gradient descent method ofestimating tissue mass and the thermal resistance scale factor inaccordance with further embodiments of the present disclosure;

FIG. 7 is a flow diagram of a non-iterative method of estimating tissuemass and the thermal resistance scale factor in accordance with stillfurther embodiments of the present disclosure;

FIG. 8 is a flow diagram of a non-iterative method of estimating tissuemass and the thermal resistance scale factor in accordance with stillfurther embodiments of the present disclosure; and

FIG. 9 is a flow diagram of a non-iterative method of estimating tissuemass and the thermal resistance scale factor in accordance with stillfurther embodiments of the present disclosure.

DETAILED DESCRIPTION

For sealing algorithms, it is desirable to determine the mass of tissuegrasped between the jaw members of an electrosurgical instrument,because the tissue mass is one of the main variations during sealing. Ingeneral, smaller masses need a small amount of energy to avoid overcooking, while larger masses need more energy to achieve a tissuetemperature within a reasonable amount of time. Also, tissue temperatureand pressure are significant factors which determine seal performance.

It is also desirable to determine tissue temperature during the sealprocedure without expensive temperature sensors built into the sealinginstruments. This can be accomplished by determining the thermalresistance scale factors or the heat transfer coefficients between thetissue and the seal plates, which is dependent on the surface area ofthe tissue. Once the thermal resistance scale factors or heat transfercoefficients are determined, then the tissue temperature can be modeledusing a known input energy.

The systems and methods according to the present disclosure estimate themass of tissue being treated based on the changes in tissue impedanceand determine the thermal resistance scale factor (k) or the thermalcoefficient of heat transfer between the tissue being treated and theenergy delivery device, e.g., the seal plate, so that tissue temperaturecan be estimated over a cycle of an electrosurgical procedure, e.g., asealing cycle. The mass of the tissue, the thermal resistance scalefactor (k), and the thermal coefficient of heat transfer are estimatedby modeling the temperatures of the tissue and the energy deliverydevice that is used to transmit electrosurgical energy to the tissueusing a set or system of differential equations.

The set of differential equations incorporates physical characteristicsof the tissue and the energy delivery device. The physicalcharacteristics include the specific heat of the tissue, the heatconductivity between the tissue and electrodes or antennas of the energydelivery device, and the relationship between changes in tissueresistance and the energy supplied to the tissue. The estimated mass,the estimated thermal resistance scale factor, and/or the estimatedthermal coefficient of heat transfer may be incorporated into algorithmsfor controlling energy delivery to the tissue.

The estimated mass and the estimated thermal coefficient of heattransfer or the thermal resistance scale factor may also be used topredict tissue temperature up to the point of loss of mass (either wateror tissue). Once it is determined that there is a loss of mass, the massand the thermal coefficient of heat transfer, or the thermal resistancescale factor may be further estimated to determine the loss in mass andto predict temperature above the boiling point of water or heat-relatedtissue mass loss (e.g., due to squeezing tissue between the jaw membersof the electrosurgical forceps).

Estimates of the tissue mass may also be useful in surgical proceduresthat employ surgical staplers. The tissue mass may be used to determinethe tissue thickness or size so that the surgical stapler and itsstaples can be properly configured to staple the tissue. Otherwise, ifthe tissue is too thin, a normal size staple may damage the tissue and,if the tissue is too thick, a normal size staple may not be effectivefor stapling the tissue.

Estimates of tissue mass may also be used in ablation procedures toadjust the microwave energy delivered to the tissue. Otherwise, too muchenergy delivered to a small mass would damage surrounding tissue and toolittle energy delivered to a large mass would not be sufficient forablating tissue.

As described above, the systems and methods for estimating tissue massand the thermal coefficient of heat transfer or the thermal resistancescale factor may be incorporated into any type of surgical device fortreating tissue. For purposes of illustration and in no way limiting thescope of the appended claims, the systems and methods for estimatingtissue mass and the thermal coefficient of heat transfer or the thermalresistance scale factor are described in the present disclosure in thecontext of electrosurgical systems.

FIG. 1 illustrates an electrosurgical system 100 in accordance with someembodiments of the present disclosure. The electrosurgical system 100includes an electrosurgical generator 102 which generateselectrosurgical energy to treat tissue of a patient. The electrosurgicalgenerator 102 generates an appropriate level of electrosurgical energybased on the selected mode of operation (e.g., cutting, coagulating,ablating, or sealing) and/or the sensed voltage and current waveforms ofthe electrosurgical energy. The electrosurgical system 100 may alsoinclude a plurality of output connectors corresponding to a variety ofenergy delivery devices, e.g., electrosurgical instruments.

The electrosurgical system 100 further includes a number of energydelivery devices. For example, system 100 includes monopolarelectrosurgical instrument 110 having an electrode for treating tissueof the patient (e.g., an electrosurgical cutting probe or ablationelectrode, also known as an electrosurgical pencil) with a return pad120. The monopolar electrosurgical instrument 110 can be connected tothe electrosurgical generator 102 via one of the plurality of outputconnectors. The electrosurgical generator 102 may generateelectrosurgical energy in the form of radio frequency (RF) energy. Theelectrosurgical energy is supplied to the monopolar electrosurgicalinstrument 110, which applies the electrosurgical energy to treat thetissue. The electrosurgical energy is returned to the electrosurgicalgenerator 102 through the return pad 120. The return pad 120 provides asufficient contact area with the patient's tissue so as to minimize therisk of tissue damage due to the electrosurgical energy applied to thetissue.

The electrosurgical system 100 also includes a bipolar electrosurgicalinstrument 130. The bipolar electrosurgical instrument 130 can beconnected to the electrosurgical generator 102 via one of the pluralityof output connectors. The electrosurgical energy is supplied to one ofthe two jaw members of the bipolar electrosurgical instrument 130, isapplied to treat the tissue, and is returned to the electrosurgicalgenerator 102 through the other of the two jaw members.

The electrosurgical generator 102 may be any suitable type of generatorand may include a plurality of connectors to accommodate various typesof electrosurgical instruments (e.g., monopolar electrosurgicalinstrument 110 and bipolar electrosurgical instrument 130). Theelectrosurgical generator 102 may also be configured to operate in avariety of modes, such as ablation, cutting, coagulation, and sealing.The electrosurgical generator 102 may include a switching mechanism(e.g., relays) to switch the supply of RF energy among the connectors towhich various electrosurgical instruments may be connected. For example,when an electrosurgical instrument 110 is connected to theelectrosurgical generator 102, the switching mechanism switches thesupply of RF energy to the monopolar plug. In embodiments, theelectrosurgical generator 102 may be configured to provide RF energy toa plurality instruments simultaneously.

The electrosurgical generator 102 includes a user interface havingsuitable user controls (e.g., buttons, activators, switches, or touchscreens) for providing control parameters to the electrosurgicalgenerator 102. These controls allow the user to adjust parameters of theelectrosurgical energy (e.g., the power level or the shape of the outputwaveform) so that the electrosurgical energy is suitable for aparticular surgical procedure (e.g., coagulating, ablating, sealing, orcutting). The energy delivery devices 110 and 130 may also include aplurality of user controls. In addition, the electrosurgical generator102 may include one or more display screens for displaying a variety ofinformation related to operation of the electrosurgical generator 102(e.g., intensity settings and treatment complete indicators).

FIG. 2 is a block diagram of generator circuitry 200 of theelectrosurgical generator 102 of FIG. 1 and an energy delivery device295 connected to the generator circuitry 200. The generator circuitry200 includes a low frequency (LF) rectifier 220, a preamplifier 225, anRF amplifier 230, a plurality of sensors 240, analog-to-digitalconverters (ADCs) 250, a controller 260, a hardware accelerator 270, aprocessor subsystem 280, and a user interface (UI) 290. Theelectrosurgical generator 102 by way of the generator circuitry 200 isconfigured to connect to an alternating current (AC) power source 210,such as a wall power outlet or other power outlet, which generates AChaving a low frequency (e.g., 25 Hz, 50 Hz, or 60 Hz). The AC powersource 210 provides AC power to the LF rectifier 220, which converts theAC to direct current (DC).

The direct current (DC) output from the LF rectifier 220 is provided tothe preamplifier 225 which amplifies the DC to a desired level. Theamplified DC is provided to the RF amplifier 230, which includes adirect current-to-alternating current (DC/AC) inverter 232 and aresonant matching network 234. The DC/AC inverter 232 converts theamplified DC to an AC waveform having a frequency suitable for anelectrosurgical procedure (e.g., 472 kHz, 29.5 kHz, and 19.7 kHz).

The appropriate frequency for the electrosurgical energy may differbased on electrosurgical procedures and modes of electrosurgery. Forexample, nerve and muscle stimulations cease at about 100,000 cycles persecond (100 kHz) above which point some electrosurgical procedures canbe performed safely; i.e., the electrosurgical energy can pass through apatient to targeted tissue with minimal neuromuscular stimulation. Forexample, typically ablation procedures use a frequency of 472 kHz. Otherelectrosurgical procedures can be performed at frequencies lower than100 kHz, e.g., 29.5 kHz or 19.7 kHz, with minimal risk of damagingnerves and muscles. The DC/AC inverter 232 can output AC signals withvarious frequencies suitable for electrosurgical operations.

As described above, the RF amplifier 230 includes a resonant matchingnetwork 234. The resonant matching network 234 is coupled to the outputof the DC/AC inverter 232 to match the impedance at the DC/AC inverter232 to the impedance of the tissue so that there is maximum or optimalpower transfer between the generator circuitry 200 and the tissue.

The electrosurgical energy provided by the DC/AC inverter 232 of the RFamplifier 230 is controlled by the controller 260. The voltage andcurrent waveforms of the electrosurgical energy output from the DC/ACinverter 232 are sensed by the plurality of sensors 240 and provided tothe controller 260, which generates control signals to control theoutput of the preamplifier 225 and the output of the DC/AC inverter 232.The controller 260 also receives input signals via the user interface(UI) 290. The UI 290 allows a user to select a type of electrosurgicalprocedure (e.g., monopolar or bipolar) and a mode (e.g., coagulation,ablation, sealing, or cutting), or input desired control parameters forthe electrosurgical procedure or the mode.

The plurality of sensors 240 sense voltage and current at the output ofthe RF amplifier 230. The plurality of sensors 240 may include two ormore pairs or sets of voltage and current sensors that provide redundantmeasurements of the voltage and current. This redundancy ensures thereliability, accuracy, and stability of the voltage and currentmeasurements at the output of the RF amplifier 230. In embodiments, theplurality of sensors 240 may include fewer or more sets of voltage andcurrent sensors depending on the application or the design requirements.The plurality of sensors 240 may measure the voltage and current outputat the output of the RF amplifier 230 and from other components of thegenerator circuitry 200 such as the DC/AC inverter 232 or the resonantmatching network 234. The plurality of sensors 240 that measures thevoltage and current may include any known technology for measuringvoltage and current including, for example, a Rogowski coil.

The DC/AC inverter 232 is electrically coupled to the energy deliverydevice 295 which may be a bipolar electrosurgical instrument 130 of FIG.1, which has two jaw members to grasp and treat tissue with the energyprovided by the DC/AC inverter 232.

The energy delivery device 295 includes temperature sensors 297 and twojaw members 299. An electrode is disposed on each of the two jaw members299. The temperature sensors 297 may measure the temperatures of thetissue and the electrodes of the two jaw members 299. At least one ofthe temperature sensors 297 may be disposed on the energy deliverydevice 295 so that the at least one of the temperature sensors 297 canmeasure tissue temperature. At least another one of the temperaturesensors 297 may be disposed on each jaw member of the bipolarelectrosurgical instrument 130 in thermal communication with anelectrode of each jaw member so that the temperatures of the jaw memberscan be measured. The temperature sensors 297 may employ any knowntechnology for sensing or measuring temperature. For example, thetemperature sensors 297 may include resistance temperature detectors,thermocouples, thermostats, thermistors, or any combination of thesetemperature sensing devices.

The sensed temperatures, voltage, and current are fed toanalog-to-digital converters (ADCs) 250. The ADCs 250 sample the sensedtemperatures, voltage, and current to obtain digital samples of thetemperatures of the tissue and the jaw members and the voltage andcurrent of the RF amplifier 230. The digital samples are processed bythe controller 260 and used to generate a control signal to control theDC/AC inverter 232 of the RF amplifier 230 and the preamplifier 225. TheADCs 250 may be configured to sample outputs of the plurality of sensors240 and the plurality of the temperature sensors 297 at a samplingfrequency that is an integer multiple of the RF frequency.

As shown in FIG. 2, the controller 260 includes a hardware accelerator270 and a processor subsystem 280. As described above, the controller260 is also coupled to a UI 290, which receives input commands from auser and displays output and input information related tocharacteristics of the electrosurgical energy (e.g., selected powerlevel). The hardware accelerator 270 processes the output from the ADCs250 and cooperates with the processor subsystem 280 to generate controlsignals.

The hardware accelerator 270 includes a dosage monitoring and control(DMAC) 272, an inner power control loop 274, a DC/AC inverter controller276, and a preamplifier controller 278. All or a portion of thecontroller 260 may be implemented by a field programmable gate array(FPGA), an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), and/or a microcontroller.

The DMAC 272 receives samples of the temperatures of the tissue and thejaw members from the ADCs 250 and estimates a mass of the tissue and athermal resistance scale factor between the tissue and the jaw members,as described in greater detail below. The DMAC 272 also calculates powerof the energy provided to the tissue based on the sensed voltage andcurrent. The DMAC 272 then provides the estimated mass of the tissue andthe thermal resistance scale factor to the inner power control loop 274,which generates a control signal for the DC/AC inverter controller 276based on the estimated mass and the estimated thermal resistance scalefactor. The DC/AC inverter controller 276 in turn generates a firstpulse-width modulation (PWM) control signal to control the output of theDC/AC inverter 232.

The processor subsystem 280 includes an outer power control loop 282, astate machine 284, and a power setpoint circuit 286. The processorsubsystem 280 generates a second PWM control signal based on the outputof the DMAC 272 and parameters (e.g., electrosurgical mode) selected bythe user via the UI 290. Specifically, the parameters selected by theuser are provided to the state machine 284 which determines a state ormode of the generator circuitry 200. The outer power control loop 282uses this state information and the output from the DMAC 272 todetermine control data. The control information is provided to the powersetpoint circuit 286 which generates a power setpoint based on thecontrol data. The preamplifier controller 278 uses the power setpoint togenerate an appropriate PWM control signal for controlling thepreamplifier 225 to amplify the DC output from the LF rectifier 220 to adesired level. If the user does not provide operational parameters tothe state machine 284 via the UI 290, then the state machine 284 maymaintain or enter a default state.

In other embodiments, the energy delivery device 295 may not include thetemperature sensors 297. In those embodiments, the controller 260 of thegenerator circuitry 200 estimates changes in tissue impedance by using aforward difference equation or an equation relating temperature changesto changes in tissue impedance as described in more detail below.

FIG. 3 shows a more detailed functional diagram of the hardwareaccelerator 270 of FIG. 2. The hardware accelerator 270 implements thosefunctions of the generator circuitry 200 that may have specialprocessing requirements such as high processing speeds. The hardwareaccelerator 270 includes the DMAC 272, the inner power control loop 274,the DC/AC inverter controller 276, and the preamplifier controller 278.

The DMAC 272 includes a plurality of analog-to-digital converter (ADC)controllers, e.g., four ADCs 312 a-312 d but not limited to this number,a digital signal processor 314, an RF data registers 316, and DMACregisters 318. The ADC controllers 312 a-312 d control the operation ofthe ADCs 250, which convert sensed temperatures, voltage, and currentinto digital data which is then provided to the digital signal processor314 that implements digital signal processing functions, some of whichare described in more detail below.

The sensed temperatures, voltage, and current are input to the ADCs 250,which sample the sensed temperatures, voltage, and current. The ADCcontrollers 312 a-312 d provide operational parameters, including apredetermined sampling rate, to the ADCs 250 so that the ADCs samplesynchronously the temperatures of the tissue and the jaw members, thevoltage, and the current at a predetermined sampling rate, i.e., apredetermined number of digital samples per second, or predeterminedsampling period. The ADC controllers 312 a-312 d may be configured tocontrol the ADCs 250 so that the sampling period corresponds to aninteger multiple of the RF frequency of the electrosurgical energy.

The digital data obtained by sampling the sensed temperatures, voltage,and current is provided to the digital signal processor 314 via the ADCcontrollers 312 a-312 d. The digital signal processor 314 uses thedigital data to estimate a mass of the tissue and a thermal resistancescale factor between the tissue and the jaw members. The estimationprocess is done by applying and combining physical principles andmathematical equations. Estimation process and derivation ofrelationship between the temperature and the mass of the tissue areexplained in detail below.

The output of the digital signal processor 314 is provided to theprocessor subsystem 280 of FIG. 2 via RF data registers 316 and signalline 379. The DMAC 272 also includes DMAC registers 318 that receive andstore relevant parameters for the digital signal processor 314. Thedigital signal processor 314 further receives signals from a PWM module346 of the DC/AC inverter controller 276 via signal line 371.

The DMAC 272 provides control signals to the inner power control loop274 via signal lines 321 a and 321 b and to the processor subsystem 280via signal line 379. As shown in FIG. 2, the inner power control loop274 processes the control signals and outputs a control signal to theDC/AC inverter controller 276. The inner power control loop 274 includesa multiplexer 324, a compensator 326, compensator registers 330, and VIlimiter 334.

The multiplexer 324 receives the estimated mass of the tissue and theestimated thermal resistance scale factor via signal lines 321 a and 321b. The multiplexer 324 also receives a select control signal, whichselects one of the inputs from the compensator registers 330 via signalline 333 a and provides the selected input to the compensator 326 viasignal line 325. Thus, the digital signal processor 314 of the DMAC 272generates control signals, which include the estimated mass and theestimated thermal resistance scale factor, and provides them to themultiplexer 324 of the inner power control loop 274 via the signal lines321 a and 321 b, respectively.

When there is a user input, the processor subsystem 280 receives theuser input and processes it with the outputs from the digital signalprocessor 314 via a signal line 379. The processor subsystem 280provides control signals via a compensator registers 330 to a VI limiter334, which corresponds to the power setpoint circuit 286 in FIG. 2. TheVI limiter 334 then provides a desired power profile (e.g., a minimumand a maximum limits of the power for a set electrosurgical mode oroperation) to the compensator 326 via signal line 335 based on the userinput and the output of the digital signal processor 314, thecompensator registers 330 also provide other control parameters to thecompensator 326 via signal line 333 b, and then the compensator 326combines all control parameters from the compensator registers 330, themultiplexer 324, and the VI limiter 334 to generate output to the DC/ACinverter controller 276 via signal line 327.

The DC/AC inverter controller 276 receives a control parameter andoutputs control signals that drives the DC/AC inverter 232. The DC/ACinverter controller 276 includes a scale unit 342, PWM registers 344,and the PWM module 346. The scale unit 342 scales the output of thecompensator registers 330 by multiplying and/or adding a number to theoutput. The scale unit 342 receives a number for multiplication and/or anumber for addition from the PWM registers 344 via signal lines 341 aand 341 b and provides its scaled result to the PWM registers 344 viasignal line 343. The PWM registers 344 store several relevant parametersto control the DC/AC inverter 232, e.g., a period, a pulse width, and aphase of the AC signal to be generated by the DC/AC inverter 232 andother related parameters. The PWM module 346 receives output from thePWM registers 344 via signal lines 345 a-345 d and generates fourcontrol signals, 347 a-347 d, that control four transistors of the DC/ACinverter 232 of the RF amplifier 230 in FIG. 2. The PWM module 346 alsosynchronizes its information with the information in the PWM registers344 via a register sync signal 347.

The PWM module 346 further provides control signals to the compensator326 of the inner power control loop 274. The processor subsystem 280provides control signals to the PWM module 346. In this way, the DC/ACinverter controller 276 can control the DC/AC inverter 232 of the RFamplifier 230 with integrated internal input (i.e., processed resultsfrom the plurality of sensors by the DMAC 272) and external input (i.e.,processed results from the user input by the processor subsystem 280).

The processor subsystem 280 also sends the control signals to thepreamplifier controller 278 via signal line 373. The preamplifiercontroller 278 processes the control signals and generates anothercontrol signal so that the preamplifier 225 amplifies direct current toa desired level suitable for being converted by the RF amplifier 230.The Preamplifier controller 278 includes PWM registers 352 and a PWMmodule 354. The PWM registers 352 receive outputs from the processorsubsystem 280 via signal line 373, stores relevant parameters as the PWMregisters 344 does, and provides the relevant parameters to the PWMmodule 354 via signal lines 353 a-353 d. The PWM module 354 also sends aregister sync signal to the PWM registers 352 via signal line 357 andgenerates four control signals, 355 a-355 d, that control fourtransistors of the preamplifier 225 in FIG. 2.

FIG. 4A shows a front cross-sectional view of a jaw member assembly 400of the electrosurgical forceps of FIG. 1, which incorporate temperaturesensors. The jaw member assembly 400 may form part of the energydelivery device 295 of FIG. 2. The jaw member assembly 400 includes jawmembers 412, 414. Jaw member 412 includes active electrodes 420 fordelivering electrosurgical energy to tissue, and a tissue temperaturesensor 430 for sensing the temperature of tissue disposed between jawmembers 412, 414. Jaw member 414 includes a return electrode 425 and aplurality of temperature sensors 440 for sensing the temperature of jawmember 414. Jaw members 412, 414 may be formed of insulating materialsand are coupled to one another via a pivot (not shown) to permitmovement of jaw members 412, 414 between an open position and anapproximately closed position for grasping tissue between the jawmembers 412, 414. The tissue temperature sensor 430 and the plurality ofjaw member temperature sensors 440 are coupled to the controller 260 ofthe generator circuitry 200 and send signals representing sensedtemperatures of the tissue and the jaw members to the ADCs 250 thatsample the sensed temperature signals. A sampling rate of the sensedtemperatures may be controlled by a portion of the ADC controllers 312a-312 d of the DMAC 272. In this way, the controller 260 processes thedigitally sampled temperatures of the tissue and the jaw members. Thenumber of sensors of the tissue and of the plurality of sensors of thejaw members can be one or more based on the needs of the electrosurgery.

FIG. 4B shows a perspective view of a surgical stapler 450. The surgicalstapler 450 includes a handle assembly 455 and an elongated body 460. Adisposable loading unit 465 is releasably secured to a distal end ofelongated body 460. Disposable loading unit 465 includes an end effector470 having a staple cartridge assembly 472 housing a plurality ofsurgical staples not shown) and an anvil 474 movably secured in relationto staple cartridge assembly 472 which is shown in expanded mode. Staplecartridge assembly 472 includes a tissue contacting surface 476 andanvil 474 includes a tissue contacting surface 478 juxtaposed to tissuecontacting surface 476 of staple cartridge assembly 472.

Handle assembly 455 includes a stationary handle member 480, a movablehandle member 482, and a barrel portion 484. A rotatable member 486 ispreferably mounted on the forward end of barrel portion 484 tofacilitate rotation of elongated body 460 with respect to handleassembly 455. An articulation lever 488 is also mounted on the forwardend of the barrel portion 484 and adjacent to the rotatable member 486to facilitate articulation of end effector 470.

The surgical stapler 450 may include a plurality of sensing devices 490as shown in FIGS. 4B and 4C. For example, sensing devices 490 can beprovided along the length of tissue contacting surface 478 of anvil 474,along the length of tissue contacting surface 476 of staple cartridgeassembly 472, on disposable loading unit 465, on elongated body 460,and/or on handle assembly 455.

The sensing devices enable the measurement of various parameters ofsurgical stapler 450, such as temperatures of and temperature changesbetween tissue contacting surfaces 476 and 478 of surgical stapler 450.The sensed temperature and temperature changes may be used to estimatethe mass of the tissue disposed between the tissue contacting surfaces476 and 478. The estimated mass may then be used to determine thethickness or size of the tissue so that an operator of the stapler canproperly configure the stapler, e.g., adjust the distance between thetissue contacting surfaces 476 and 478 or select a staple of suitablesize.

As described above, the mass of the tissue and the thermal resistancescale factor (k) are estimated by modeling the temperatures of thetissue and the energy-based surgical device that is used to heat thetissue using a set or system of differential equations. This set ofdifferential equations may be derived as follows. The temperature of thetissue increases when heat is added to the tissue, e.g., via electricalheating of the tissue, and the rate of change of the tissue temperatureis related to the specific heat of the tissue. The following equationdescribes the relationship between the change in tissue temperature, themass of the tissue, and the added heat:

$\begin{matrix}{{{\frac{}{t}T} = \frac{\frac{}{t}Q}{C_{p}M}},} & (1)\end{matrix}$

where

$\frac{}{t}T$

represents the change in tissue temperature with respect to time,

$\frac{}{t}Q$

represents the heat added to the tissue in joules with respect to time,C_(p) is the specific heat in joules/kg, and M is the mass of the tissuein kg. Equation (1) may be rewritten as:

$\begin{matrix}{{{T} = \frac{Q}{C_{p}M}},} & (2)\end{matrix}$

where dT represents the change in temperature and dQ represents the heatadded to the tissue. In other words, the change in temperature dT is theratio between the heat added to the tissue dQ and the product of thespecific heat C_(p) and the mass M of the tissue.

The conduction of heat through tissue depends on the water content inthe tissue and the mobility of ions in the tissue caused by theconduction of electrical energy. This relationship is given by thefollowing equation:

$\begin{matrix}{{\sigma = {\sigma_{0}\frac{W}{W_{0}}^{\alpha {({T - T_{0}})}}}},} & (3)\end{matrix}$

where σ is the current electrical conductivity in Siemens per meter(S/m), σ₀ is the initial conductivity of the tissue in S/m, W is thecurrent water content of the tissue in kg or kg/m³, W₀ is the initialwater content of the tissue in kg or kg/m³, α is a unitless temperaturecoefficient constant of ion mobility, T is the current temperature inKelvin, and T₀ is the initial temperature in Kelvin which corresponds toσ₀ and W₀. Equation (3) can be solved for the temperature change, i.e.,dT=T−T₀, yielding the following equation:

$\begin{matrix}{{T} = {\frac{1}{\alpha}{{\ln \left( \frac{W_{0}\sigma}{W\; \sigma_{0}} \right)}.}}} & (4)\end{matrix}$

The tissue impedance may be expressed as a function of conductivity bythe following equation:

$\begin{matrix}{{Z = {\frac{1}{\sigma} \cdot \frac{L}{A}}},} & (5)\end{matrix}$

where Z is the current impedance in ohms, σ is the current conductivityof the tissue in S/m, L is the length of the tissue grasped by the jawmembers in meters (m), A is the area of the tissue grasped by the jawmembers in square meters (m²). The starting impedance may similarly beexpressed by the following equation:

$\begin{matrix}{{Z_{0} = {\frac{1}{\sigma_{0}} \cdot \frac{L}{A}}},} & (6)\end{matrix}$

where Z₀ is the starting impedance in ohms and σ₀ is the startingconductivity of the tissue in S/m. Combining equations (3)-(5) resultsin the following equation:

$\begin{matrix}{{dT} = {\frac{1}{\alpha}{{\ln \left( \frac{W_{0}Z_{0}}{WZ} \right)}.}}} & (7)\end{matrix}$

A thermal resistance scale factor k between the tissue and the jawmembers of the energy-based medical device may be incorporated intoequation (7) to accommodate different ratios of heat conductivitybetween the tissue and the jaw members of the energy-based medicaldevice. Thus, after incorporating the thermal resistance scale factor k,equation (7) becomes:

$\begin{matrix}{{dT} = {\frac{k}{\alpha}{{\ln \left( \frac{W_{0}Z_{0}}{WZ} \right)}.}}} & (8)\end{matrix}$

Equation (8) is an accurate estimate of the temperature change at thestart of a sealing or ablation procedure. However, equation (8) may notbe as accurate thereafter. For example, as the jaw members of the energydelivery device transfers electrical energy to the tissue, theelectrical energy is converted into heat due to the thermal resistanceof the tissue and the temperature of the tissue rises. As thetemperature of the tissue rises, water in the tissue starts to vaporizeinto the environment. Equation (8) is not accurate in this situationbecause equation (8) assumes that there is no water loss from the tissueand thus no change in impedance.

When water loss starts to occur, the tissue temperature does not changeuntil most of water in the tissue is vaporized. Thus, equation (8) maynot be accurate in this situation either. However, assuming that thewater content of the tissue to be treated is very small and vaporizationof water is also negligible, equation (8) can be expressed as follows:

$\begin{matrix}{{dT} = {\frac{k}{\alpha}{\ln \left( \frac{Z_{0}}{Z} \right)}}} & (9)\end{matrix}$

Equation (9) shows that the change in tissue temperature is dependentupon change in impedance assuming that there is negligible water loss.Thus, equation (9) may be used to determine changes in tissuetemperature based upon measurements of the tissue impedance (i.e.,measurements of Z₀ and Z) due to a pulse of RF energy applied to thetissue. The current impedance can be obtained by solving equation (9)for Z, which results in the following equation:

$\begin{matrix}{Z = {Z_{0} \cdot {^{- \frac{\alpha \; {dT}}{k}}.}}} & (10)\end{matrix}$

The mass of the tissue M may be obtained by combining equations (2) and(9) and solving the combined equations for M, as follows:

$\begin{matrix}{\frac{dQ}{C_{p}M} = {\left. {\frac{k}{\alpha}{\ln \left( \frac{Z_{0}}{Z} \right)}}\Rightarrow M \right. = {\frac{\alpha \; {dQ}}{C_{p}k\; {\ln \left( \frac{Z_{0}}{Z} \right)}}.}}} & (11)\end{matrix}$

Again, equation (11) assumes that water content of the tissue to betreated is very small and that the loss of water, i.e., the vaporizationof water, is also negligible. Equation (11) does not contemplate energyloss between the tissue and the jaw members of the energy-based medicaldevice and, thus, is not sufficient to model the temperature changebetween the tissue and the jaw members of the energy-based medicaldevice during a sealing or ablation procedure.

Assuming that the mass and the thermal resistance scale factor are knownor estimated, temperatures of the tissue or the jaw members of theenergy-based medical device can be modeled by using heat-relatedequations. According to the Newton's law of cooling, the heat loss fromthe tissue depends on the temperature difference between the tissue andthe jaw members of an energy-based medical device, which is representedby the equation:

$\begin{matrix}{{{\frac{}{t}T_{t}} = {- {k\left( {{T_{t}(t)} - {T_{j}(t)}} \right)}}},} & (12)\end{matrix}$

where T_(t)(t) is the tissue temperature (the subscript t refers totissue), T_(j)(t) is the temperature of the jaw members (the subscript jrefers to jaw members), and k is a thermal resistance scale factor whichis

$\frac{hA}{C_{p}M}$

between the tissue and the jaw members, where h is a heat transfercoefficient, A is the surface area of the jaw member which is in contactwith tissue, C_(p) is specific heat, and M is the mass of the tissuegrasped by the jaw members. Additionally, the change in the tissuetemperature depends on the change of heat added and lost to the jawmembers, which is represented by the following equation:

$\begin{matrix}{{{\frac{}{t}T_{t}} = \frac{{\frac{}{t}{Q_{add}(t)}} + {\frac{}{t}{Q_{loss}(t)}}}{C_{p_{t}}M_{t}}},} & (13)\end{matrix}$

Equation (13) is another way to represent equation (2). In someembodiments, the energy-based medical device may be configured tominimize heat loss from the tissue to the jaw members using equation(13). Thus, assuming that heat loss is minimized and further assumingthat heat loss to the environment is considered negligible, equation(13) becomes:

$\begin{matrix}{{\frac{}{t}T_{t}} = {\frac{\frac{}{t}{Q_{add}(t)}}{C_{p_{t}}M_{t}}.}} & (14)\end{matrix}$

The basic temperature difference equations of the tissue and of the jawmembers may be obtained by combining equations (12) and (13) as follows:

$\begin{matrix}{{{\frac{}{t}T_{t}} = {{- {k\left( {{T_{t}(t)} - {T_{j}(t)}} \right)}} + \frac{\frac{}{t}{Q_{add}(t)}}{C_{p_{t}}M_{t}}}},\mspace{14mu} {or}} & (15) \\{T_{j} = {{T_{t}(t)} + \frac{\frac{}{t}{T_{t}(t)}}{k} - {\frac{\frac{}{t}{Q_{add}(t)}}{{kC}_{p_{t}}M_{t}}.}}} & (16)\end{matrix}$

The temperature change ratio of the jaw members depends on thetemperature change between the tissue temperature and the jaw members'temperature. Even though the jaw members are exposed to the environment,heat added from and lost to the environment is assumed to be negligiblebecause of insulation of the jaw member assembly 400 and the large massof the jaw members as compared to the tissue. Thus, the mathematicalterm representing the heat added to the environment can be ignored orconsidered as 0. As a result, the basic temperature difference equationof the jaw members is:

$\begin{matrix}{{\frac{}{t}T_{j}} = {{k\left( {{T_{t}(t)} - {T_{j}(t)}} \right)}.}} & (17)\end{matrix}$

Applying equation (16) to equation (17) eliminates the term T_(j) andresults in the following equation for the tissue:

$\begin{matrix}{{\frac{}{t}\left( {{T_{t}(t)} + \frac{\frac{}{t}{T_{t}(t)}}{k} - \frac{\frac{}{t}{Q_{add}(t)}}{k\; C_{p_{t}}M_{t}}} \right)} = {k\left( {{T_{t}(t)} - \left( {{T_{t}(t)} + \frac{\frac{}{t}{T_{t}(t)}}{k} - \frac{\frac{}{t}{Q_{add}(t)}}{k\; C_{p_{t}}M_{t}}} \right)} \right)}} & (18)\end{matrix}$

A simplified version of equation (18) may be expressed in the form of asecond-order differential equation, which is given by:

$\begin{matrix}{{{\frac{^{2}}{t^{2}}{T_{t}(t)}} + {2k\frac{}{t}{T_{t}(t)}}} = {\frac{{\frac{^{2}}{t^{2}}{Q_{add}(t)}} + {k\frac{}{t}{Q_{add}(t)}}}{C_{p_{t}}M_{t}}.}} & (19)\end{matrix}$

In a similar way, equation (17) may be applied to equation (16) toeliminate the term T_(t) and the resulting equation may be simplyexpressed in the form a second order differential equation given by:

$\begin{matrix}{{{\frac{^{2}}{t^{2}}{T_{j}(t)}} + {2k\frac{}{t}{T_{j}(t)}}} = {\frac{k\frac{}{t}{Q_{add}(t)}}{C_{p_{t}}M_{t}}.}} & (20)\end{matrix}$

Equations (19) and (20) may be used to predict temperatures of the jawmembers and the tissue, respectively. Since the rate of heat change is aform of power, i.e.,

${{\frac{}{t}{Q(t)}} = {{Pwr}(t)}},$

equations (19) and (20) can also be written as:

$\begin{matrix}{{{{\frac{^{2}}{t^{2}}{T_{j}(t)}} + {2\; k\frac{}{t}{T_{j}(t)}}} = \frac{{kPwr}(t)}{C_{p_{t}}M_{t}}}{and}} & (21) \\{{{{\frac{^{2}}{t^{2}}{T_{j}(t)}} + {2\; k\frac{}{t}{T_{j}(t)}}} = \frac{{kPwr}(t)}{C_{p_{t}}M_{t}}},} & (22)\end{matrix}$

where Pwr(t) is the power that may be any forcing function, such as astep response, exponential, sinusoid, single pulse, two pulses, or anyother suitable signal for sealing and ablation procedures. The power iscontrolled by the controller 260 of the generator circuitry 200. Theplurality of sensors 240 sense the voltage and current at the output ofthe RF Amp 230 and the DMAC 272 calculates power by multiplying thevoltage and current of in any suitable ways.

Equations (21) and (22) are second-order differential equations that maybe used to predict the temperatures of the tissue and the jaw membersbased upon a known thermal resistance scale factor and a known mass ofthe jaw members. Conversely, equations (21) and (22) may be used toestimate the thermal resistance scale factor and the mass of the tissuebased upon known or measured temperatures of the tissue and jaw members.

The solution to the system of second-order differential equations givenby equations (21) and (22) is:

$\begin{matrix}{\mspace{20mu} {{T_{t}(t)} = \frac{\begin{matrix}{P - {^{{- 2} \cdot k \cdot t}P} + {2{P \cdot k \cdot t}} + {2C_{p_{t}}M_{t}T_{j_{0}}k} +} \\{{2C_{p_{t}}M_{t}T_{t_{0}}k} - {2^{{- 2} \cdot k \cdot t}C_{p_{t}}M_{t}T_{j_{0}}k} + {2^{{- 2} \cdot k \cdot t}C_{p_{t}}M_{t}T_{t_{0}}k}}\end{matrix}}{4C_{p_{t}}M_{t}k}}} & (23) \\{\mspace{20mu} {and}} & \; \\{{{T_{j}(t)} = \frac{\begin{matrix}{{^{{- 2} \cdot k \cdot t}P} - P + {2{P \cdot k \cdot t}} + {2C_{p_{t}}M_{t}T_{j_{0}}k} +} \\{{2C_{p_{t}}M_{t}T_{t_{0}}k} + {2^{{- 2} \cdot k \cdot t}C_{p_{t}}M_{t}T_{j_{0}}k} - {2^{{- 2} \cdot k \cdot t}C_{p_{t}}M_{t}T_{t_{0}}k}}\end{matrix}}{4C_{p_{t}}M_{t}k}},} & (24)\end{matrix}$

where T_(t) ₀ and T_(j) ₀ are the initial temperatures of the tissue andthe jaw members, respectively, and P is the power level Pwr(t).

Assuming that the initial temperatures of the tissue and the jaw membersare zero, equations (23) and (24) become:

$\begin{matrix}{{T_{t}(t)} = {\frac{P - {^{{- 2} \cdot k \cdot t}P} + {2{P \cdot k \cdot t}}}{4C_{p_{t}}M_{t}k} = {\frac{P \cdot t}{2C_{p_{t}}M_{t}} + \frac{P}{4C_{p_{t}}M_{t}k} - \frac{P\; ^{{- 2} \cdot k \cdot t}}{4C_{p_{t}}M_{t}k}}}} & (25) \\{\mspace{20mu} {and}} & \; \\{{T_{j}(t)} = {\frac{{^{{- 2} \cdot k \cdot t}P} - P + {2{P \cdot k \cdot t}}}{4C_{p_{t}}M_{t}k} = {\frac{P \cdot t}{2C_{p_{t}}M_{t}} - \frac{P}{4C_{p_{t}}M_{t}k} + \frac{P\; ^{{- 2} \cdot k \cdot t}}{4C_{p_{t}}M_{t}k}}}} & (26)\end{matrix}$

The rate of change in tissue temperature is determined by taking thederivative of equation (25) with respect to time, which results in theequation:

$\begin{matrix}{{\frac{}{t}{T_{t}(t)}} = {\frac{{^{{- 2} \cdot k \cdot t}P} + P}{2C_{p_{t}}M_{t}} = {\frac{P}{2C_{p_{t}}M_{t}} + \frac{P\; ^{{- 2} \cdot k \cdot t}}{2C_{p_{t}}M_{t}}}}} & (27)\end{matrix}$

After the method 500 starts, an index i is initialized to zero in step505 and is incremented by one in step 510. In step 515, the controller260 causes the generator circuitry 200 to supply electrosurgical energy,e.g., alternating current, at a desired power level to the tissue to betreated via the jaw members of an electrosurgical instrument coupled tothe generator. The electrosurgical energy causes the temperature of thetissue to rise. As the temperature of the tissue rises, heat istransferred from the tissue to the jaw members, which causes thetemperature of the jaw members to rise. Since the parameters of the jawmembers are known, temperature changes in the jaw members can becalculated by using equation (26).

Various iterative and non-iterative methods may be employed to estimatethe thermal resistance scale factor and the mass of tissue usingequations (25) and (27). FIG. 5 illustrates a method 500 for estimatingthe thermal resistance scale factor and the mass of tissue using aniterative gradient descent algorithm. The method 500 utilizes equations(25) and (27) in a discrete sense. In particular, the method 500 uses achange in temperature or a temperature difference dT_(t)(t) rather thana derivative of the temperature

$\frac{}{t}{T_{t}(t)}$

for each iteration.

In the case of measuring the thermal resistance scale factor and themass of tissue, the temperature T_(i) of the tissue is sensed by aplurality of temperature sensors 297 in step 520. In step 525, atemperature difference dT_(i), which is equal to the difference betweenthe current tissue temperature T_(i) and the previous tissue temperatureT_(i-1), is calculated. When the index i is equal to one, dT_(i) iszero, and when the index i is greater than one, dT_(i) is equal to thedifference between current tissue temperature T_(i) and the previoustissue temperature T_(i-1).

In embodiments, the temperature difference dT_(i) may be determinedbased on changes in measured or estimated tissue impedance. In thiscase, the method 500 may first estimate tissue impedance after applyingpower to the tissue in step 510 and then may estimate temperaturechanges dT_(i) based on changes in the estimated tissue impedance byusing equation (9).

In step 530, the index i is compared to the number of desired iterationsN. If the index i is less than the number of iterations N, steps 510 to525 are repeated. If the index i is equal to N, the mass M and thethermal resistance scale factor K are initialized in step 535 and thenestimated using an iterative method in step 545.

In step 545, the true mass and the true thermal resistance scale factorare estimated by using gradient descent which adjusts a derivative stepof the mass and thermal resistance scale factor based on errors and isdescribed in more detail in FIGS. 6B-6D. Afterwards, the controller 260controls levels of power to adjust the temperature of the tissue and thejaw members during electrosurgery based on the estimates of the mass andthe thermal resistance scale factor in step 550.

FIG. 6A is a flow diagram of a method 600 of determining startingestimates of the mass and the thermal resistance scale factor for thegradient descent method. After starting, an index i is initialized tozero in step 605. Also, an array of masses M_(i) and thermal resistancescale factors K_(i) corresponding to measured temperatures T_(i) are setto non-zero values, and an array of temperature differences dT_(i) areset to zero in step 605.

In step 610, the index i is incremented by one. In step 615, an estimateof the mass M_(i) is calculated based on the thermal resistance scalefactor K_(i) using the following equation:

$\begin{matrix}{{M_{i} = \frac{P + {2{P \cdot K_{i} \cdot t_{i}}} - {P \cdot ^{{- 2} \cdot K_{i} \cdot t_{i}}}}{4C_{p}K_{i}T_{i}}},} & (31)\end{matrix}$

where P is the power level, K_(t) is the thermal resistance scale factorat index i, t_(i) is time in seconds at index i, C_(p) is the specificheat constant for tissue, and T_(i) is the sensed temperature at indexi. Equation (31) is derived from equation (25) by solving for the massM. In step 620, the temperature difference dT_(i) at index i iscalculated according to the following equation:

$\begin{matrix}{{{dT}_{i} = \frac{{^{{- 2} \cdot K_{i} \cdot t_{i}}P} + P}{2C_{p}M_{i}}},} & (32)\end{matrix}$

which is the discretized version of equation (27).

In step 625, an estimate of the thermal resistance scale factor K_(t) iscalculated based on the previously calculated mass M_(t) and thetemperature difference dT_(i) using the following equation:

$\begin{matrix}{{K_{i} = \frac{- {\ln\left( \frac{{2\; C_{p}M_{i}{dT}_{i}} - P}{P} \right)}}{2t}},} & (33)\end{matrix}$

which is derived from equation (32) by solving for thermal resistancescale factor K_(i). Next, in step 630, the temperature difference dT_(i)is recalculated based on the estimates of the mass M_(i) and the thermalresistance scale factor K_(i).

In step 635, the index i is compared to a predetermined number N, whichis the length of the array of samples of the tissue temperature T_(i).If it is determined that the index i is less the predetermined number N,steps 610 through 630 are repeated. Otherwise, in step 640, an estimatedstarting mass M and starting thermal resistance scale factor K are setfor the gradient descent algorithm based on the masses M_(i) and thethermal resistance scale factors K_(i). For example, the maximum of thearray of masses M_(i) and the maximum of the array of thermal resistancescale factors K_(i) may be set as the estimated starting mass M and theestimated starting thermal resistance scale factor estimate K,respectively. Alternatively, the minimum of the array of masses M_(i)and the maximum of the array of thermal resistance scale factors K_(i)may be set as the estimated starting mass M and the estimated startingthermal resistance scale factor K, respectively. In a furtheralternative, the average of the array of masses M_(i) and the average ofthe array of thermal resistance scale factor K_(i) may be set as theestimated starting mass M and the estimated starting thermal resistancescale factor K for the gradient descent algorithm.

FIGS. 6B-6D are flow diagrams illustrating the gradient descent methodthat estimates the mass M and the thermal resistance scale factor Kaccording to embodiments of the present disclosure. As shown in FIG. 6B,in step 645, an index i is initialized to zero, a derivative step forthe estimated mass is initialized to a non-zero value, e.g., 1×10⁻⁷, anda derivative step for the estimated thermal resistance scale factor isinitialized to a non-zero value, e.g., 1.0. According to the gradientdescent algorithm, the derivative step for the mass estimate is used toincrease or decrease the mass estimate so that the mass estimate reachesa value close to the actual value, and the derivative step for theestimated thermal resistance scale factor is used to increase ordecrease the estimated thermal resistance scale factor so that theestimated thermal resistance scale factor reaches a value close to theactual value. In step 650, the index i is incremented until the indexreaches a predetermined number N.

In step 655, a second mass estimate M_(s) is calculated by summing thestarting or first mass estimate M and the derivative step for the massand a second thermal resistance scale factor K_(s) is calculated bysumming the starting or first thermal resistance scale factor estimate Kand the derivative step for the thermal resistance scale factor.

FIG. 6C shows a flow diagram that continues from the flow diagram ofFIG. 6B for determining the mass estimate according to the gradientdescent method. In step 660, first estimates for the temperature and thetemperature difference are calculated according to the following forwarddifference equations:

$\begin{matrix}{{{\hat{T}}_{i} = \frac{P - {e^{{- 2} \cdot K \cdot t_{i}}P} + {2{P \cdot K \cdot t_{i}}}}{4C_{p}{MK}}},{and}} & (34) \\{{{d{\hat{T}}_{i}} = \frac{{^{{- 2} \cdot K \cdot t_{i}}P} + P}{2C_{p}M}},} & (35)\end{matrix}$

where M is the first mass estimate. In step 662, a second estimate ofthe temperature and the temperature difference, {circumflex over(T)}s_(i) and d{circumflex over (T)}s_(i), are calculated based on thesecond mass estimate M_(s) using equations (34) and (35).

Next, in step 664, a first temperature error between the sensedtemperature T_(i) and the first estimated temperature {circumflex over(T)}_(i) is calculated, and a second temperature error between thesensed temperature T_(i) and the second estimated temperature{circumflex over (T)}s_(i) is calculated. Similarly, in step 666, afirst temperature difference error between the sensed temperaturedifference dT_(i) and the first estimated temperature differenced{circumflex over (T)}_(i) is calculated, and a second temperaturedifference error between the sensed temperature difference dT_(i) andthe second estimated temperature difference d{circumflex over (T)}s_(i)is calculated.

In step 668, a derivative of the error is calculated. The derivative ofthe error may be calculated by finding the difference between the sum ofthe first errors and the sum of the second errors and dividing theresulting difference by the sum of the first errors.

In step 670, simulated annealing is performed by first determiningwhether the sign of the derivative of the error has changed. If the signof the derivative of the error has changed, the derivative step size isreduced in step 672 and the first estimated mass is set equal to thesecond estimated mass. If the sign of the derivative of the error hasnot changed, the first estimated mass is set equal to the secondestimated mass. The simulated annealing process reduces the derivativestep size as the error approaches a predetermined value to prevent theiterative method from oscillating when the derivative step size is toolarge.

FIG. 6D shows a flow diagram that continues from the flow diagram ofFIG. 6B for determining the thermal resistance scale factor estimateaccording to the gradient descent method. In step 680, third estimatesfor the temperature and the temperature difference are calculatedaccording to equations (34) and (35) where K is the first thermalresistance scale factor estimate. In step 682, a fourth estimate of thetemperature and the temperature difference, {circumflex over (T)}s_(i)and d{circumflex over (T)}s_(i), are calculated based on the secondthermal resistance scale factor estimate K_(s) using equations (34) and(35).

Next, in step 684, a third temperature error between the sensedtemperature T_(i) and the third estimated temperature {circumflex over(T)}_(i) is calculated, and a fourth temperature error between thesensed temperature T_(i) and the fourth estimated temperature{circumflex over (T)}s_(i) is calculated. Similarly, in step 686, athird temperature difference error between the sensed temperaturedifference dT_(i) and the third estimated temperature differenced{circumflex over (T)}_(i) is calculated, and a fourth temperaturedifference error between the sensed temperature difference dT_(i) andthe fourth estimated temperature difference d{circumflex over (T)}s_(i)is calculated.

In step 688, a derivative of the error is calculated. The derivative ofthe error may be calculated by finding the difference between the sum ofthe third errors and the sum of the fourth errors and dividing theresulting difference by the sum of the third errors.

In step 690, simulated annealing is performed by first determiningwhether the sign of the derivative of the error has changed. If the signof the derivative of the error has changed, the derivative step size isreduced in step 692 and the first estimated thermal resistance scalefactor K is set equal to the second estimated thermal resistance scalefactor K_(s). If the sign of the derivative of the error has notchanged, the first estimated thermal resistance scale factor K is setequal to the second estimated thermal resistance scale factor K_(s) instep 694.

After step 674 and 694 of FIGS. 6C and 6D, respectively, are performed,it is determined whether the derivative error for the mass estimate M isless than a first threshold value and the derivative error for thethermal resistance scale factor estimate K is less than a secondthreshold value in step 696. If it is determined that the derivativeerrors for M and K are less than respective first and second thresholdvalues, the gradient descent method is ended because the estimates ofthe mass and thermal resistance scale factor are deemed to besufficiently close to the actual mass and thermal resistance scalefactor. Otherwise, the index i is compared with the predetermined numberN in step 698. If the index i is less than the predetermined number N,the gradient descent method returns to step 650 and all steps from 650to 698 in FIGS. 6B-6D are repeated until the index i reaches thepredetermined number N or until the conditions described in step 696 aremet.

The gradient descent method described in FIGS. 6A-6D is an accurate androbust method for estimating the mass and thermal resistance scalefactor of tissue and/or an energy-based medical instrument. In otherembodiments, simpler methods, such as the non-iterative methodillustrated in FIG. 7, may be employed for estimating the mass and thethermal resistance scale factor.

In steps 505-530, tissue temperatures T_(i) are sensed and temperaturedifferences dT_(i) are calculated as described above with respect toFIG. 5. The non-iterative method illustrated in FIG. 7 makes anassumption closely related to the exponentially decreasingcharacteristics of an exponential term in the temperature and thetemperature difference equations (25) and (27). The equation for themass of tissue M_(t) is given by:

$\begin{matrix}{{M_{t} = \frac{P + {2{P \cdot k \cdot t}} - {P \cdot ^{{- 2} \cdot k \cdot t}}}{4C_{p_{t}}{kT}_{t}}},} & (36)\end{matrix}$

which is derived from equation (25) by solving for the mass of tissueM_(t).

The exponential term e^(−2·k·t) approaches zero as time t increases andbecomes negligible after a certain time period. Thus, assuming that anappropriate amount of time elapses, equation (36) can be simplified byremoving the exponential term as follows:

$\begin{matrix}{M_{t} = {\frac{P + {2{P \cdot k \cdot t}}}{4C_{p_{t}}T_{t}k}.}} & (37)\end{matrix}$

Also, the temperature difference equation (32) is a reducing functionwhich reduces from its maximum value when t is equal to zero to itsminimum value when t is equal to infinity. Equation (32) is reduced byabout 63% of the maximum value when:

2·k·t=1.  (38)

Solving equation (38) for the thermal resistance scale factor K_(t)results in the following equation:

$\begin{matrix}{{k = \frac{1}{2\; t_{63}}},} & (39)\end{matrix}$

where t₆₃ is the time at which the maximum rate of change in tissuetemperature is reduced by about 63% toward the minimum rate of change.Thus, the mass can be calculated using time t₆₃ and equations (37) and(39).

Referring again to FIG. 7, the thermal resistance scale factor k firstinvolves determining the maximum and the minimum tissue temperaturedifferences for a predetermined period during which energy is applied tothe tissue in step 735. Next, in step 740, the 63% reduction pointbetween the maximum and the minimum temperature differences iscalculated according to the following equation:

max(dT _(i))−(max(dT _(i))−min(dT _(i)))·0.63,  (40)

where dT_(i) is an array of tissue temperature differences for thepredetermined period during which energy is applied to the tissue.

In step 745, it is determined the time t₆₃ at which the 63% reductionoccurs. The time t₆₃ may be determined by using a linear interpolationalgorithm where dT₁ is the x-axis and the time index is the y-axis. Thelinear interpolation algorithm is illustrated by the following equation:

dT _(i)(t ₆₃)=max(dT _(i))−(max(dT _(i))−min(dT _(i)))·0.63.  (41)

The thermal resistance scale factor k is then calculated by using thetime t₆₃ and equation (39).

In step 750, the mass may be estimated using equation (37) at a time5*t₆₃ when the temperature difference dT_(i) is theoretically decreasedby about 99%. A time other than 5*t₆₃, e.g., 4*t₆₃, may be used toestimate the mass depending on the system requirements. In someembodiments of the non-iterative method, the last elements of the sensedtemperature array and the corresponding time index array are used tocalculate the mass estimate. The controller 260 then uses the estimatedmass and the estimated thermal resistance scale factor in controllingthe generator.

In another embodiment, a heat transfer coefficient h, rather than thethermal resistance scale factor, is estimated to take into considerationthe heat transfer characteristics of the jaw members. The thermalresistance scale factor of equation (17) is defined by the heat transfercharacteristics of the jaw members as follows:

$\begin{matrix}{k = \frac{hA}{C_{p_{j}}M_{j}}} & (42)\end{matrix}$

Substituting equation (42) for the thermal resistance scale factor inequation (17) results in the following equation:

$\begin{matrix}{{\frac{}{t}T_{j}} = {\frac{hA}{C_{p_{j}}M_{j}}\left( {{T_{t}(t)} - {T_{j}(t)}} \right)}} & (43)\end{matrix}$

where C_(p) _(j) is the specific heat of the jaw members and M_(j) isthe mass of the jaw members.

By employing the specific heat and the mass of the jaw members, equation(16) becomes:

$\begin{matrix}{T_{j} = {{T_{t}(t)} + \frac{C_{p_{t}}M_{t}\frac{}{t}{T_{t}(t)}}{hA} - \frac{\frac{}{t}{Q_{add}(t)}}{hA}}} & (44)\end{matrix}$

Applying equation (44) to equation (17) to eliminate the term T_(j)results in the following equation:

$\begin{matrix}{{\frac{}{t}\left( {{T_{t}(t)} + \frac{C_{p_{t}}M_{t}\frac{}{t}{T_{t}(t)}}{h\; A} - \frac{\frac{}{t}{Q_{add}(t)}}{h\; A}} \right)} = {\frac{h\; A}{C_{p_{j}}M_{j}}{\left( {{T_{t}(t)} - \left( {{T_{t}(t)} + \frac{C_{p_{t}}M_{t}\frac{}{t}{T_{t}(t)}}{h\; A} - \frac{\frac{}{t}{Q_{add}(t)}}{h\; A}} \right)} \right).}}} & (45)\end{matrix}$

A simplified version of equation (45) can be expressed in the form of asecond-order differential equation, which is given by:

$\begin{matrix}{{{\frac{^{2}}{t^{2}}{T_{t}(t)}} + {\frac{h\; {A \cdot \left( {\frac{C_{p_{t}}M_{t}}{C_{p_{j}}M_{j}} + 1} \right)}}{C_{p_{t}}M_{t}}\frac{}{t}{T_{t}(t)}}} = {\frac{h\; A\frac{}{t}{Q_{add}(t)}}{C_{p_{j}}M_{j}C_{p_{t}}M_{t}}.}} & (46)\end{matrix}$

Equation (17) may be applied to equation (43) to eliminate the termT_(t) and the resulting equation can be expressed in the form of asecond order differential equation as follows:

$\begin{matrix}{{{\frac{^{2}}{t^{2}}{T_{t}(t)}} + {\frac{h\; {A \cdot \left( {\frac{C_{p_{j}}M_{j}}{C_{p_{t}}M_{t}} + 1} \right)}}{C_{p_{j}}M_{j}}\frac{}{t}{T_{t}(t)}}} = {\frac{h\; A\frac{}{t}{Q_{add}(t)}}{C_{p_{j}}M_{j}C_{p_{t}}M_{t}}.}} & (47)\end{matrix}$

Equations (46) and (47) may be used to predict temperatures of the jawmembers and the tissue, respectively. Since the rate of heat change is aform of power, i.e.,

${{\frac{}{t}{Q(t)}} = {Pwr}},$

equations (46) and (47) can also be written as:

$\begin{matrix}{{{{\frac{^{2}}{t^{2}}{T_{t}(t)}} + {\frac{h\; {A \cdot \left( {\frac{C_{p_{t}}M_{t}}{C_{p_{j}}M_{j}} + 1} \right)}}{C_{p_{t}}M_{t}}\frac{}{t}{T_{t}(t)}}} = \frac{h\; A\; {Pwr}}{C_{p_{j}}M_{j}C_{p_{t}}M_{t}}}{and}} & (48) \\{{{{\frac{^{2}}{t^{2}}{T_{t}(t)}} + {\frac{h\; {A \cdot \left( {\frac{C_{p_{j}}M_{j}}{C_{p_{t}}M_{jt}} + 1} \right)}}{C_{p_{j}}M_{j}}\frac{}{t}{T_{t}(t)}}} = \frac{h\; A\; {Pwr}}{C_{p_{j}}M_{j}C_{p_{t}}M_{t}}},} & (49)\end{matrix}$

where Pwr is the power that may be any forcing function, such as a stepresponse, exponential, sinusoid, single pulse, two pulses, or any othersuitable signal for sealing and ablation procedures. The power iscontrolled by the controller 260 of the generator circuitry 200. Theplurality of sensors 240 sense the voltage and current at the output ofthe RF Amp 230 and the DMAC 272 calculates power based on the sensedvoltage and current.

Second-order differential equations (48) and (49) can be used to predictthe temperatures of the tissue and the jaw members based upon a knownheat transfer coefficient and a known mass. Conversely, the equations(48) and (49) can be used to estimate the heat transfer coefficient andthe mass based upon measured temperatures of the tissue and jaw members.

Solutions to the system of second-order differential equations given byequations (48) and (49) are very complex. However, assuming that theinitial temperatures of the tissue and the jaw members are zero andsimplifying the solutions to equations (48) and (49), results in thefollowing equations for the temperature of the tissue and the jawmembers:

$\begin{matrix}{{T_{t}(t)} = {\frac{{Pwr} \cdot t}{{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} + {\frac{C_{p_{j}}^{2}{M_{j}^{2} \cdot {Pwr}}}{h\; {A\left( {{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} \right)}^{2}}\left( {1 - {^{- \frac{h\; {A \cdot t}}{C_{p_{j}}M_{j}}} \cdot ^{- \frac{h\; {A \cdot t}}{C_{p_{t}}M_{t}}}}} \right)}}} & (50) \\{\mspace{79mu} {and}} & \; \\{{T_{j}(t)} = {\frac{{Pwr} \cdot t}{{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} - {\frac{C_{p_{j}}M_{j}C_{p_{t}}{M_{t} \cdot {Pwr}}}{h\; {A\left( {{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} \right)}^{2}}\left( {1 - {^{- \frac{h\; {A \cdot t}}{C_{p_{j}}M_{j}}} \cdot ^{- \frac{h\; {A \cdot t}}{C_{p_{t}}M_{t}}}}} \right)}}} & (51)\end{matrix}$

The rate of change of tissue temperature is determined by taking thederivative of equation (50) with respect to time, which results in thefollowing equation:

$\begin{matrix}{{\frac{}{t}{T_{t}(t)}} + \frac{{Pwr} \cdot \left( {{C_{p_{t}}M_{t}} + {C_{p_{j}}{M_{j} \cdot ^{- \frac{h\; {A \cdot t}}{C_{p_{j}}M_{j}}} \cdot ^{- \frac{h\; {A \cdot t}}{C_{p_{t}}M_{t}}}}}} \right)}{C_{p_{t}}{M_{t}\left( {{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} \right)}}} & (52)\end{matrix}$

Time plays a significant role in estimating the mass of the tissue beingtreated and the heat transfer coefficient h. The exponential terms

$^{- \frac{{hA} \cdot t}{C_{p_{j}}M_{j}}} \cdot ^{- \frac{{hA} \cdot t}{C_{p_{t}}M_{t}}}$

in equations (50) and (52) are equal to one when the time is zero andbecome negligible as time increases. Thus, when t=0, equation (52)becomes the following equation:

$\begin{matrix}{{\frac{}{t}{T_{t}(T)}} = {\frac{Pwr}{C_{p_{t}}M_{t}}.}} & (53)\end{matrix}$

Equation (53) implies that the mass of the tissue may be estimated withthe first estimation of the change in temperature when the estimationtime is close to zero. The heat transfer coefficient h may be estimatedwhen time is large while the mass may be estimated when time is verysmall. When t is large enough to make the exponential terms negligiblysmall, equation (50) simplifies to the following equation:

$\begin{matrix}{{T_{t}(t)} = {\frac{{Pwr} \cdot t}{{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} + {\frac{C_{p_{j}}^{2}{M_{j}^{2} \cdot {Pwr}}}{h\; {A\left( {{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} \right)}^{2}}.}}} & (54)\end{matrix}$

Equation (54) can be solved for h to obtain the following equation:

$\begin{matrix}{h = {\frac{C_{p_{j}}^{2}{M_{j}^{2} \cdot {Pwr}}}{A\left( {{{T_{t}(t)}\left( {{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} \right)^{2}} - {{Pwr} \cdot t \cdot \left( {{C_{p_{j}}M_{j}} + {C_{p_{t}}M_{t}}} \right)}} \right)}.}} & (55)\end{matrix}$

In this way, the mass and the heat transfer coefficient of the tissuecan be determined and used to estimate the temperature of the tissue.

A method of estimating tissue mass is illustrated in the flow diagram ofFIG. 8. In step 805, a test signal is applied to the tissue to cause ameasurable amount of tissue heating. In other words, the test signal isprovided to cause a measurable change in tissue impedance so that ameasurable change in temperature can be obtained. The test signal mayinclude a step, an exponential signal, a sinusoid, a single pulse,multiple pulses, or any other signal suitable for causing a measurableamount of tissue heating. In step 810, a change in tissue temperature ismeasured as soon as possible after providing the test signal to thetissue.

In the case where temperature sensors are not used, a change in tissueimpedance is estimated and then a change in the tissue temperature isestimated based on the estimated change in tissue impedance by usingequation (9).

In step 815, the controller 260 determines a time value that is largeenough so that the exponential term of the tissue temperature equation(51) becomes negligibly small. In step 820, tissue temperature ismeasured at the time value.

In step 825, by using equation (54), the tissue mass is estimated basedon the power supplied by the power output stage of the generator, therate of temperature change, and the specific heat of the tissue. Aclosed form solution for estimating the tissue mass is given by theequation:

$\begin{matrix}{M_{t} = {\frac{Pwr}{C_{p_{t}}\frac{}{t}{T_{t}(t)}}.}} & (56)\end{matrix}$

In step 830, the heat transfer coefficient is estimated by usingequation (55), which is based on tissue temperature, specific heat ofthe tissue and the jaw members, power, masses of the tissue and the jawmembers, and the determined time. In this way, the mass and the heattransfer coefficient of the tissue may be estimated.

The time for estimating the heat transfer coefficient may be shortenedby using equation (53) for estimating the heat transfer coefficient. Thecontroller 260 measures the rate of temperature change at two times,namely, t₁ and t₂, and uses the ratio between the two rates. The ratiomay be simplified and expressed as follows:

$\begin{matrix}{{ratio} = {\frac{\frac{}{t}{T_{t\; 2}(t)}}{\frac{}{t}{T_{t\; 1}(t)}} = {\frac{\left( {{C_{p_{t}}M_{t}} + {C_{p_{j}}{M_{j} \cdot ^{- \frac{h\; {A \cdot t_{2}}}{C_{p_{j}}M_{j}}} \cdot ^{- \frac{h\; {A \cdot t_{2}}}{C_{p_{t}}M_{t}}}}}} \right)}{\left( {{C_{p_{t}}M_{t}} + {C_{p_{j}}{M_{j} \cdot ^{- \frac{h\; {A \cdot t_{1}}}{C_{p_{j}}M_{j}}} \cdot ^{- \frac{h\; {A \cdot t_{1}}}{C_{p_{t}}M_{t}}}}}} \right)}.}}} & (57)\end{matrix}$

Equation (57) can be solved for h as follows:

$\begin{matrix}{h = {{- \frac{{C_{p_{t}}M_{t}} + {C_{p_{j}}M_{j}}}{{A\left( {{C_{p_{t}}M_{t}} + {C_{p_{j}}M_{j}}} \right)} \cdot t_{2}}}{\ln\left( \frac{\begin{matrix}{C_{p_{t}}{M_{t}\left( {{C_{p_{j}}M_{j}\frac{}{t}{{T_{t\; 1}(t)} \cdot {ratio}}} -} \right.}} \\{{Pwr} + {C_{p_{t}}M_{t}\frac{}{t}{{T_{t\; 1}(t)} \cdot {ratio}}}}\end{matrix}}{C_{p_{j}}{M_{j} \cdot {Pwr}}} \right)}}} & (58)\end{matrix}$

The benefit of this approach is that the controller 260 does not have towait until a time when the exponential term decreases to a negligiblysmall value before obtaining an estimate of the tissue mass and the heattransfer coefficient.

FIG. 9 shows a flow diagram illustrating a method of estimating the heattransfer coefficient. In step 905, a test signal is provided to thetissue being treated. In step 910, the controller 260 estimates thetemperature change dT₁ of the tissue at the beginning of a tissuetreatment procedure. As described above, the temperature change dT₁ ofthe tissue can be measured directly by temperature sensors or can beestimated by using the equation (9) after estimating the change intissue impedance. The earlier dT₁ is measured, the more accurate theestimate of the mass.

In step 915, the controller 260 determines a time t₂ for measuringtemperature change and, in step 920, the controller 260 estimates thetemperature change dT₂ at time t₂. In step 925, the tissue mass ismeasured by using equation (54) and, in step 930, the heat transfercoefficient h is estimated by using equation (58). In this way, the timefor estimation can be shortened

The methods of the present disclosure may further consider the heattransfer between the jaw members and the environment. This isrepresented by heat transfer equation (17), which may be expressed as:

$\begin{matrix}{\frac{T_{j}}{t} = {{k\left( {{T_{t}(t)} - {T_{j}(t)}} \right)} + {k_{e}\left( {T_{e} - {T_{j}(t)}} \right)}}} & (59)\end{matrix}$

where T_(e) represents the constant temperature of the environment andk_(e) represents a thermal resistance scale factor from the jaw membersto the environment. Even assuming that the initial conditions of thetemperature of the tissue and the jaw members are zero and making othersimplifications, the closed form solutions to the system of differentialequations is very complex. However, general approximation methods can beutilized to estimate the tissue mass and the heat transfer coefficient.

Although the illustrative embodiments of the present disclosure havebeen described herein with reference to the accompanying drawings, it isto be understood that the disclosure is not limited to those preciseembodiments, and that various other changes and modification may beeffected therein by one skilled in the art without departing from thescope or spirit of the disclosure.

What is claimed is:
 1. A system comprising: a generator including: anoutput stage configured to generate energy to treat tissue; and acontroller coupled to the output stage and configured to control theoutput stage; and an energy delivery device coupled to the generator andconfigured to treat the tissue, the energy delivery device including: anelectrode configured to deliver the generated energy to the tissue; aplurality of temperature sensors including first and second temperaturesensors, the plurality of temperature sensors configured to be inthermal communication with the tissue and the electrode, wherein thecontroller comprises: a signal processor configured to estimate mass ofthe tissue and a thermal resistance scale factor of the tissue and theelectrode based on the sensed temperatures of the tissue and theelectrode; and an output controller configured to generate a controlsignal to control the output stage based on the estimated mass and theestimated thermal resistance scale factor.
 2. The system according toclaim 1, wherein the energy delivery device is electrosurgical forcepshaving a first jaw member and a second jaw member, wherein the electrodeis disposed in the first jaw member, and wherein and the firsttemperature sensor is disposed in the first jaw member.
 3. The system ofclaim 2, further comprising a return electrode disposed in the secondjaw member, wherein the second temperature sensor is disposed in thesecond jaw member in thermal communication with the return electrode. 4.The system according to claim 1, wherein the generator is a microwavegenerator and the energy delivery device is a tissue ablationinstrument.
 5. The system according to claim 1, wherein the signalprocessor is further configured to: sample temperatures of the tissuesensed by the plurality of temperature sensors a predetermined number oftimes; calculate a temperature difference for each sampled temperature;and estimate mass of the tissue and a thermal resistance scale factorbetween the tissue and the electrode based on the sampled temperaturesand the calculated temperature difference.
 6. The system according toclaim 5, wherein the signal processor is further configured to: select amaximum and a minimum among the calculated temperature difference;calculate a time at which a predetermined percentage reduction occursfrom the maximum to the minimum; calculate an estimate of a thermalresistance scale factor based on the calculated time; and calculate amass estimate based on the estimate of the thermal resistance scalefactor and the calculated time.
 7. The system according to claim 6,wherein the predetermined percentage is about sixty three percent. 8.The system according to claim 6, wherein calculating the mass estimatecalculates the mass at a second time longer than the time.
 9. The systemaccording to claim 1, wherein the temperature sensor is selected fromthe group consisting of a resistance temperature detector, athermocouple, a thermostat, and a thermistor.
 10. A method ofcontrolling a system that includes a generator that generates energy totreat tissue, the method comprising: providing a test signal to thetissue; sensing temperatures of the tissue and an electrode of thesystem a predetermined number of times; calculating a temperaturedifference for each sensed temperature value; estimating mass of thetissue and a thermal resistance scale factor between the tissue and theelectrode; and generating a control signal to control an output stage ofthe generator based on the estimated mass of the tissue and theestimated thermal resistance scale factor.
 11. The method according toclaim 10, wherein the mass of the tissue and the thermal resistancescale factor are estimated based on the sensed temperatures and thecalculated changes in temperature.
 12. The method according to claim 11,wherein estimating the mass of the tissue and a thermal resistance scalefactor includes: calculating an initial mass estimate and an initialthermal resistance scale factor estimate for each sensed temperature;selecting one of the initial mass estimates as a starting mass estimateand one of the initial thermal resistance scale factor estimates as astarting thermal resistance scale factor estimate; setting a firstderivative step for the mass estimate and a second derivative step forthe thermal resistance scale factor estimate; and performing aniterative method to estimate the mass and thermal resistance scalefactor of the tissue using the starting mass estimate, the startingthermal resistance scale factor estimate, and the first and secondderivative steps.
 13. The method according to claim 12, wherein theiterative method is a gradient descent method.
 14. The method accordingto claim 12, wherein the starting mass estimate is a maximum among theinitial masses and the starting thermal resistance scale factor estimateis a maximum among the initial thermal resistance scale factors.
 15. Themethod according to claim 12, wherein the starting mass estimate is aminimum among the initial masses and the starting thermal resistancescale factor estimate is a maximum among the initial thermal resistancescale factors.
 16. The method according to claim 12, wherein thestarting mass estimate is an average of the initial masses and thestarting thermal resistance scale factor estimate is an average of theinitial thermal resistance scale factors.
 17. The method according toclaim 13, wherein performing the gradient descent method includes:calculating a first temperature estimate and a first temperaturedifference estimate based on the mass estimate and the thermalresistance scale factor estimate; calculating a second temperatureestimate and a second temperature difference estimate based on the massestimate, the thermal resistance scale factor estimate, and a firstderivative step for the mass estimate; calculating a third temperatureestimate and a third temperature difference estimate based on the massestimate, the thermal resistance scale factor estimate, and a secondderivative step for the thermal resistance scale factor estimate;calculating first errors between the sensed temperature and the firsttemperature estimate, between the sensed temperature and the secondtemperature estimate, between the sensed temperature difference and thefirst temperature difference estimate, and between the sensedtemperature difference and the second temperature difference estimate;calculating second errors between the sensed temperature and the firsttemperature estimate, between the sensed temperature and the thirdtemperature estimate, between the sensed temperature difference and thefirst temperature difference estimate, and between the sensedtemperature difference and the third temperature difference estimate;calculating a first error derivative based on the calculated firsterrors; calculating a second error derivative based on the calculatedsecond errors; calculating an updated mass estimate based on the firsterror derivative; and calculating an updated thermal resistance scalefactor estimate based on the second error derivative.
 18. The methodaccording to claim 17, wherein calculating an updated mass estimateincludes: determining whether the first error derivative changes sign;reducing the first derivative step when it is determined that the firsterror derivative changes in sign; and setting the mass estimate as thesum of the mass estimate and the first derivative step.
 19. The methodaccording to claim 17, wherein calculating an updated thermal resistancescale factor estimate includes: determining whether the second errorderivative changes in sign; reducing the second derivative step when itis determined that the second error derivative changes sign; and settingthe thermal resistance scale factor estimate as the sum of the thermalresistance scale factor estimate and the second derivative step.
 20. Themethod according to claim 17, further comprising: determining whetherthe first error derivative is less than a first threshold value and thesecond error derivative is less than a second threshold value; andstopping the gradient descent method when it is determined that thefirst error derivative is less than the first threshold value and thesecond error derivative is less than the second threshold value.
 21. Themethod according to claim 10, wherein estimating the mass of the tissueand the thermal resistance scale factor includes: selecting a maximumand a minimum among the calculated temperature difference; calculating atime at which a predetermined percentage reduction occurs from themaximum to the minimum; calculating an estimate of the thermalresistance scale factor based on the calculated time; and calculating amass estimate based on the estimate of the thermal resistance scalefactor estimate and the calculated time.
 22. The method according toclaim 21, wherein the predetermined percentage is about sixty threepercent.
 23. The method according to claim 21, wherein calculating themass estimate calculates the mass at a second time longer than thecalculated time.
 24. The method according to claim 10, wherein the massand the thermal resistance scale factor are estimated based on a systemof second-order differential equations of changes in tissue temperature.25. The system according to claim 1, wherein the energy delivery deviceis a vessel sealing device.